A predictive model for respiratory syncytial virus (RSV) hospitalisation of premature infants born at 33-35 weeks of gestational age, based on data from the Spanish FLIP study
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Background: The aim of this study, conducted in Europe, was to
develop a validated risk factor based model to predict RSV-related
hospitalisation in premature infants born 33-35 weeks' gestational
age (GA). Methods: The predictive model was developed using risk
factors captured in the Spanish FLIP dataset, a case-control study
of 183 premature infants born between 33-35 weeks' GA who were
hospitalised with RSV, and 371 age-matched controls. The model was
validated internally by 100-fold bootstrapping. Discriminant
function analysis was used to analyse combinations of risk factors
to predict RSV hospitalisation. Successive models were chosen that
had the highest probability for discriminating between hospitalised
and non-hospitalised infants. Receiver operating characteristic
(ROC) curves were plotted. Results: An initial 15 variable model
was produced with a discriminant function of 72% and an area under
the ROC curve of 0.795. A step-wise reduction exercise, alongside
recalculations of some variables, produced a final model consisting
of 7 variables: birth +/- 10 weeks of start of season, birth
weight, breast feeding for = 2 years,
family members with atopy, family members with wheeze, and gender.
The discrimination of this model was 71% and the area under the ROC
curve was 0.791. At the 0.75 sensitivity intercept, the false
positive fraction was 0.33. The 100-fold bootstrapping resulted in
a mean discriminant function of 72% (standard deviation: 2.18) and
a median area under the ROC curve of 0.785 (range: 0.768-0.790),
indicating a good internal validation. The calculated NNT for
intervention to treat all at risk patients with a 75% level of
protection was 11.7 (95% confidence interval: 9.5-13.6).
Conclusion: A robust model based on seven risk factors was
developed, which is able to predict which premature infants born
between 33-35 weeks' GA are at highest risk of hospitalisation from
RSV. The model could be used to optimise prophylaxis with
palivizumab across Europe.
develop a validated risk factor based model to predict RSV-related
hospitalisation in premature infants born 33-35 weeks' gestational
age (GA). Methods: The predictive model was developed using risk
factors captured in the Spanish FLIP dataset, a case-control study
of 183 premature infants born between 33-35 weeks' GA who were
hospitalised with RSV, and 371 age-matched controls. The model was
validated internally by 100-fold bootstrapping. Discriminant
function analysis was used to analyse combinations of risk factors
to predict RSV hospitalisation. Successive models were chosen that
had the highest probability for discriminating between hospitalised
and non-hospitalised infants. Receiver operating characteristic
(ROC) curves were plotted. Results: An initial 15 variable model
was produced with a discriminant function of 72% and an area under
the ROC curve of 0.795. A step-wise reduction exercise, alongside
recalculations of some variables, produced a final model consisting
of 7 variables: birth +/- 10 weeks of start of season, birth
weight, breast feeding for = 2 years,
family members with atopy, family members with wheeze, and gender.
The discrimination of this model was 71% and the area under the ROC
curve was 0.791. At the 0.75 sensitivity intercept, the false
positive fraction was 0.33. The 100-fold bootstrapping resulted in
a mean discriminant function of 72% (standard deviation: 2.18) and
a median area under the ROC curve of 0.785 (range: 0.768-0.790),
indicating a good internal validation. The calculated NNT for
intervention to treat all at risk patients with a 75% level of
protection was 11.7 (95% confidence interval: 9.5-13.6).
Conclusion: A robust model based on seven risk factors was
developed, which is able to predict which premature infants born
between 33-35 weeks' GA are at highest risk of hospitalisation from
RSV. The model could be used to optimise prophylaxis with
palivizumab across Europe.
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